Cover Image

FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	AbouNassar, Eman M., Elshall, Amr, Abdulah, Sameh
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

BFLN: A Blockchain-based Federated Learning Model for Non-IID Data
by: Li, Yang, et al.
Published: (2024)

FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data
by: Ng, Jer Shyuan, et al.
Published: (2025)

Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data
by: Wang, Shilong, et al.
Published: (2025)

Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data
by: Shao, Jiaqi, et al.
Published: (2023)

Optimization of Federated Learning's Client Selection for Non-IID Data Based on Grey Relational Analysis
by: Chen, Shuaijun, et al.
Published: (2023)

A Novel Approach to Translate Structural Aggregation Queries to MapReduce Code
by: Abdelmoniem, Ahmed M., et al.
Published: (2025)

Accelerating Mixed-Precision Out-of-Core Cholesky Factorization with Static Task Scheduling
by: Ren, Jie, et al.
Published: (2024)

Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
by: Lee, Seunghyun, et al.
Published: (2025)

SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
by: Xu, Yang, et al.
Published: (2023)

Cross-Layer Energy Analysis of Multimodal Training on Grace Hopper Superchips
by: Ahmed, Mahmoud, et al.
Published: (2026)

FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)

GPU-Accelerated Vecchia Approximations of Gaussian Processes for Geospatial Data using Batched Matrix Computations
by: Pan, Qilong, et al.
Published: (2024)

High-Performance Statistical Computing (HPSC): Challenges, Opportunities, and Future Directions
by: Abdulah, Sameh, et al.
Published: (2025)

GPU-Accelerated Modified Bessel Function of the Second Kind for Gaussian Processes
by: Geng, Zipei, et al.
Published: (2025)

FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence
by: Wu, Zhiyuan, et al.
Published: (2023)

FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data
by: Zhou, Yuan, et al.
Published: (2025)

Dynamic Topology Optimization for Non-IID Data in Decentralized Learning
by: Cox, Bart, et al.
Published: (2026)

Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
by: Mujtaba, Ahmed, et al.
Published: (2025)

Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data
by: Morafah, Mahdi, et al.
Published: (2024)

FedCDC: A Collaborative Framework for Data Consumers in Federated Learning Market
by: Shi, Zhuan, et al.
Published: (2025)

Scaled Block Vecchia Approximation for High-Dimensional Gaussian Process Emulation on GPUs
by: Pan, Qilong, et al.
Published: (2025)

pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2024)

Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study
by: Yang, Yuwen, et al.
Published: (2024)

FedFQ: Federated Learning with Fine-Grained Quantization
by: Li, Haowei, et al.
Published: (2024)

FedOptimus: Optimizing Vertical Federated Learning for Scalability and Efficiency
by: Shrivastava, Nikita, et al.
Published: (2025)

Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data
by: Ahn, Seyoung, et al.
Published: (2022)

Personalized Federated Learning on Data with Dynamic Heterogeneity under Limited Storage
by: Tan, Sixing, et al.
Published: (2024)

EcoFed: Efficient Communication for DNN Partitioning-based Federated Learning
by: Wu, Di, et al.
Published: (2023)

FedFog: Resource-Aware Federated Learning in Edge and Fog Networks
by: Sobati-M, Somayeh
Published: (2025)

FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud
by: Sinha, Aditya, et al.
Published: (2025)

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning
by: Lan, Muhang, et al.
Published: (2025)

FedAST: Federated Asynchronous Simultaneous Training
by: Askin, Baris, et al.
Published: (2024)

Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications
by: Zhang, Xiran, et al.
Published: (2024)

FedCod: An Efficient Communication Protocol for Cross-Silo Federated Learning with Coding
by: Yan, Peishen, et al.
Published: (2024)

FedHC: A Hierarchical Clustered Federated Learning Framework for Satellite Networks
by: Liu, Zhuocheng, et al.
Published: (2025)

FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications
by: Wilkins, Grant, et al.
Published: (2023)

FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
by: He, Yuepeng, et al.
Published: (2024)

DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
by: Zhu, Guogang, et al.
Published: (2024)

FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning
by: Tastan, Nurbek, et al.
Published: (2024)